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Stilgoe A, Favre-Bulle IA, Watson ML, Gomez-Godinez V, Berns MW, Preece D, Rubinsztein-Dunlop H. Shining Light in Mechanobiology: Optical Tweezers, Scissors, and Beyond. ACS PHOTONICS 2024; 11:917-940. [PMID: 38523746 PMCID: PMC10958612 DOI: 10.1021/acsphotonics.4c00064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 02/22/2024] [Accepted: 02/23/2024] [Indexed: 03/26/2024]
Abstract
Mechanobiology helps us to decipher cell and tissue functions by looking at changes in their mechanical properties that contribute to development, cell differentiation, physiology, and disease. Mechanobiology sits at the interface of biology, physics and engineering. One of the key technologies that enables characterization of properties of cells and tissue is microscopy. Combining microscopy with other quantitative measurement techniques such as optical tweezers and scissors, gives a very powerful tool for unraveling the intricacies of mechanobiology enabling measurement of forces, torques and displacements at play. We review the field of some light based studies of mechanobiology and optical detection of signal transduction ranging from optical micromanipulation-optical tweezers and scissors, advanced fluorescence techniques and optogenentics. In the current perspective paper, we concentrate our efforts on elucidating interesting measurements of forces, torques, positions, viscoelastic properties, and optogenetics inside and outside a cell attained when using structured light in combination with optical tweezers and scissors. We give perspective on the field concentrating on the use of structured light in imaging in combination with tweezers and scissors pointing out how novel developments in quantum imaging in combination with tweezers and scissors can bring to this fast growing field.
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Affiliation(s)
- Alexander
B. Stilgoe
- School of
Mathematics and Physics, The University
of Queensland, Brisbane, 4074, Australia
- ARC
CoE for Engineered Quantum Systems, The
University of Queensland, Brisbane, 4074, Australia
- ARC
CoE in Quantum Biotechnology, The University
of Queensland, 4074, Brisbane, Australia
| | - Itia A. Favre-Bulle
- School of
Mathematics and Physics, The University
of Queensland, Brisbane, 4074, Australia
- Queensland
Brain Institute, The University of Queensland, Brisbane, 4074, Australia
| | - Mark L. Watson
- School of
Mathematics and Physics, The University
of Queensland, Brisbane, 4074, Australia
- ARC
CoE for Engineered Quantum Systems, The
University of Queensland, Brisbane, 4074, Australia
| | - Veronica Gomez-Godinez
- Institute
of Engineering and Medicine, University
of California San Diego, San Diego, California 92093, United States
| | - Michael W. Berns
- Institute
of Engineering and Medicine, University
of California San Diego, San Diego, California 92093, United States
- Beckman
Laser Institute, University of California
Irvine, Irvine, California 92612, United States
| | - Daryl Preece
- Beckman
Laser Institute, University of California
Irvine, Irvine, California 92612, United States
| | - Halina Rubinsztein-Dunlop
- School of
Mathematics and Physics, The University
of Queensland, Brisbane, 4074, Australia
- ARC
CoE for Engineered Quantum Systems, The
University of Queensland, Brisbane, 4074, Australia
- ARC
CoE in Quantum Biotechnology, The University
of Queensland, 4074, Brisbane, Australia
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Inferring potential landscapes from noisy trajectories of particles within an optical feedback trap. iScience 2022; 25:104731. [PMID: 36034218 PMCID: PMC9400092 DOI: 10.1016/j.isci.2022.104731] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 06/27/2022] [Accepted: 07/02/2022] [Indexed: 11/22/2022] Open
Abstract
While particle trajectories encode information on their governing potentials, potentials can be challenging to robustly extract from trajectories. Measurement errors may corrupt a particle’s position, and sparse sampling of the potential limits data in higher energy regions such as barriers. We develop a Bayesian method to infer potentials from trajectories corrupted by Markovian measurement noise without assuming prior functional form on the potentials. As an alternative to Gaussian process priors over potentials, we introduce structured kernel interpolation to the Natural Sciences which allows us to extend our analysis to large datasets. Structured-Kernel-Interpolation Priors for Potential Energy Reconstruction (SKIPPER) is validated on 1D and 2D experimental trajectories for particles in a feedback trap. A feedback trap was used to generate noisy Langevin microbead trajectories The potential energy surface is recovered using a Bayesian formulation The formulation uses a structured-kernel-interpolation Gaussian process (SKI-GP) to tractably approximate Gaussian process regression for larger datasets Thanks to our adaptation of SKI-GP, we have broadened the use of Gaussian processes for natural science applications
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